Unsupervised Image Classification
28 papers with code • 7 benchmarks • 6 datasets
Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.
Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)
Benchmarks
These leaderboards are used to track progress in Unsupervised Image Classification
Libraries
Use these libraries to find Unsupervised Image Classification models and implementations
CIFAR-10
ImageNet
MNIST
SVHN
STL-10
ObjectNet
Latest papers with no code
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network
We present results of a deep photonic spiking convolutional neural network, based on two-section VCSELs, targeting image classification.
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings
We introduce three techniques to successfully train MIX'EM and avoid degenerate solutions; (i) diversify mixture components by maximizing entropy, (ii) minimize instance conditioned component entropy to enforce a clustered embedding space, and (iii) use an associative embedding loss to enforce semantic separability.
Self-supervised classification of dynamic obstacles using the temporal information provided by videos
Nowadays, autonomous driving systems can detect, segment, and classify the surrounding obstacles using a monocular camera.
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective.
